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At least 937 records · Page 52Linked to original sources

A collaborative approach for estimating terrestrial wildlife abundance

Accurately estimating abundance of wildlife is critical for establishing effective conservation and management strategies. Aerial methodologies for estimating abundance are common in developed countries, but they are often impractical for remote areas of developing countries where many of the world's endangered and threatened fauna exist. The alternative terrestrial methodologies can be constrained by limitations on access, technology, and human resources, and have rarely been comprehensively conducted for large terrestrial mammals at landscape scales. We attempted to overcome these problems by incorporating local peoples into a simultaneous point count of Asiatic wild ass (Equus hemionus) and goitered gazelle (Gazella subgutturosa) across the Great Gobi B Strictly Protected Area, Mongolia. Paired observers collected abundance and covariate metrics at 50 observation points and we estimated population sizes using distance sampling theory, but also assessed individual observer error to examine potential bias introduced by the large number of minimally trained observers. We estimated 5671 (95% CI = 3611–8907) wild asses and 5909 (95% CI = 3762–9279) gazelle inhabited the 11,027 km 2 study area at the time of our survey and found that the methodology developed was robust at absorbing the logistical challenges and wide range of observer abilities. This initiative serves as a functional model for estimating terrestrial wildlife abundance while integrating local people into scientific and conservation projects. This, in turn, creates vested interest in conservation by the people who are most influential in, and most affected by, the outcomes.

Biological Conservation↗

Avoidance of unconventional oil wells and roads exacerbates habitat loss for grassland birds in the North American great plains

Oil development in the Bakken shale region has increased rapidly as a result of new technologies and strong demand for fossil fuel. This region also supports a particularly high density and diversity of grassland bird species, which are declining across North America. We examined grassland bird response to unconventional oil extraction sites (i.e. developed with hydraulic fracturing and horizontal drilling techniques) and associated roads in North Dakota. Our goal was to quantify the amount of habitat that was indirectly degraded by oil development, as evidenced by patterns of avoidance by birds. Grassland birds avoided areas within 150 m of roads (95% CI: 87–214 m), 267 m of single-bore well pads (95% CI: 157–378 m), and 150 m of multi-bore well pads (95% CI: 67–233 m). Individual species demonstrated variable tolerance of well pads. Clay-colored sparrows ( Spizella pallida ) were tolerant of oil-related infrastructure, whereas Sprague's pipit ( Anthus spragueii ) avoided areas within 350 m (95% CI: 215–485 m) of single-bore well pads. Given these density patterns around oil wells, the potential footprint of any individual oil well, and oil development across the region, is greatly multiplied for sensitive species. Efforts to reduce new road construction, concentrate wells along developed corridors, combine numerous wells on multi-bore pads rather than build many single-bore wells, and to place well pads near existing roads will serve to minimize loss of suitable habitat for birds. Quantifying environmental degradation caused by oil development is a critical step in understanding how to better mitigate harm to wildlife populations.

Biological Conservation↗

A reply to Iversen et al.'s comment “Monitoring of animal abundance by environmental DNA - An increasingly obscure perspective”

We appreciate the conversation put forward by Iversen et al. (2015) in their response to our article “Quantification of eDNA shedding rates from invasive bighead carp Hypophthalmichthys nobilis and silver carp Hypophthalmichthys molitrix ” in the 2015 environmental DNA special issue of Biological Conservation. We agree with Iversen et al.'s concern about overly optimistic conclusions that could be drawn from the current eDNA literature. One hope for eDNA technology is that it can be used in estimating abundance or population density. Evidence suggests that eDNA measurements correlate with total biomass (Takahara et al., 2012) rather than abundance. We demonstrate a similar relationship between biomass and eDNA shedding rates. Nevertheless, without field testing of these methods and specific survey protocols, we cannot make strong conclusions regarding the technique's field applicability. In our manuscript, we attempted to point out areas in which more research is needed.

Biological Conservation↗

Framework for assessing and mitigating the impacts of offshore wind energy development on marine birds

Offshore wind energy development (OWED) is rapidly expanding globally and has the potential to contribute significantly to renewable energy portfolios. However, development of infrastructure in the marine environment presents risks to wildlife. Marine birds in particular have life history traits that amplify population impacts from displacement and collision with offshore wind infrastructure. Here, we present a broadly applicable framework to assess and mitigate the impacts of OWED on marine birds. We outline existing techniques to quantify impact via monitoring and modeling (e.g., collision risk models, population viability analysis), and present a robust mitigation framework to avoid, minimize, or compensate for OWED impacts. Our framework addresses impacts within the context of multiple stressors across multiple wind energy developments. We also present technological and methodological approaches that can improve impact estimation and mitigation. We highlight compensatory mitigation as a tool that can be incorporated into regulatory frameworks to mitigate impacts that cannot be avoided or minimized via siting decisions or alterations to OWED infrastructure or operation. Our framework is intended as a globally-relevant approach for assessing and mitigating OWED impacts on marine birds that may be adapted to existing regulatory frameworks in regions with existing or planned OWED.

Biological Conservation↗

Tracking data highlight the importance of human-induced mortality for large migratory birds at a flyway scale

Human-induced direct mortality affects huge numbers of birds each year, threatening hundreds of species worldwide. Tracking technologies can be an important tool to investigate temporal and spatial patterns of bird mortality as well as their drivers. We compiled 1704 mortality records from tracking studies across the African-Eurasian flyway for 45 species, including raptors, storks, and cranes, covering the period from 2003 to 2021. Our results show a higher frequency of human-induced causes of mortality than natural causes across taxonomic groups, geographical areas, and age classes. Moreover, we found that the frequency of human-induced mortality remained stable over the study period. From the human-induced mortality events with a known cause ( n = 637), three main causes were identified: electrocution (40.5 %), illegal killing (21.7 %), and poisoning (16.3 %). Additionally, combined energy infrastructure-related mortality (i.e., electrocution, power line collision, and wind-farm collision) represented 49 % of all human-induced mortality events. Using a random forest model, the main predictors of human-induced mortality were found to be taxonomic group, geographic location (latitude and longitude), and human footprint index value at the location of mortality. Despite conservation efforts, human drivers of bird mortality in the African-Eurasian flyway do not appear to have declined over the last 15 years for the studied group of species. Results suggest that stronger conservation actions to address these threats across the flyway can reduce their impacts on species. In particular, projected future development of energy infrastructure is a representative example where application of planning, operation, and mitigation measures can enhance bird conservation.

Biological Conservation↗

Social.Water - A crowdsourcing tool for environmental data acquisition

Remote telemetry has a long history of use for collection of environmental measurements. With the rise of mobile phones and SMS text-messaging capacity, many members of the general pubic carry communications equipment in their pockets at all times. Enabling the general public to provide environmental data through text messages has the potential both to provide additional data to scientific projects and also to raise awareness of the projects through participation. Hydrologic measurements – some of which can be made without training, involve a single measurement, and are often made in rural areas – are well-suited to text-message conveyance. Many other environmental measurements are similarly well-suited for this technology. Social.Water is a software package, written in Python, that collects, parses, and categorizes text messages sent to a dedicated phone number, updates a simple database, and posts both graphical results and the database on the Web. Social.Water was designed as the backend to the Crowdhydrology project and is written in an object-oriented design that makes customization and modification straightforward.

Computers & Geosciences↗

HERA: A dynamic web application for visualizing community exposure to flood hazards based on storm and sea level rise scenarios

The Hazard Exposure Reporting and Analytics (HERA) dynamic web application was created to provide a platform that makes research on community exposure to coastal-flooding hazards influenced by sea level rise accessible to planners, decision makers, and the public in a manner that is both easy to use and easily accessible. HERA allows users to (a) choose flood-hazard scenarios based on sea level rise and storm assumptions, (b) appreciate the modeling uncertainty behind a chosen hazard zone, (c) select one or several communities to examine exposure, (d) select the category of population or societal asset, and (e) choose how to look at results. The application is designed to highlight comparisons between (a) varying levels of sea level rise and coastal storms, (b) communities, (c) societal asset categories, and (d) spatial scales. Through a combination of spatial and graphical visualizations, HERA aims to help individuals and organizations to craft more informed mitigation and adaptation strategies for climate-driven coastal hazards. This paper summarizes the technologies used to maximize the user experience, in terms of interface design, visualization approaches, and data processing.

Computers & Geosciences↗

Acoustic Sediment Estimation Toolbox (ASET): A software package for calibrating and processing TRDI ADCP data to compute suspended-sediment transport in sandy rivers

Quantifying suspended-sediment transport is critical for a variety of disciplines related to the management of water resources. However, the number of gauging stations and monitoring networks in most rivers around the world is insufficient to improve understanding of river dynamics and support water resource management decisions. This is mainly due to the high operational costs and intensive labor involved in traditional sediment measurement techniques, especially in sand bed rivers where coarse material varies spatially in the river cross section. Recently, the acoustic surrogate method has received attention as a potentially accurate surrogate technology for estimating suspended-sediment concentrations. In addition, the acoustic surrogate method, through use of acoustic Doppler current profilers (ADCPs), has the advantage of being able to simultaneously measure the flow velocity field and cross-sectional area when moving-boat measurements are performed. In spite of the important advances made in the implementation of this technique, there are no widely-available, free tools for processing the ADCP acoustic signal cross section measurements which include options to extrapolate velocity and sediment in unmeasured ADCP zones and develop calibrations with physical samples. This paper presents a new software called Acoustic Sediment Estimation Toolbox (ASET), which enables the user to develop a calibration between the acoustic signal collected with a down-looking Teledyne RD Instruments ADCP and sediment concentrations determined using traditional sediment sampling techniques. Moreover, ASET software uses dynamic ADCP measurements to estimate the total suspended-sediment transport through a river cross section. The theoretical framework and data processing routines applied by each module in ASET are presented. Finally, a comparison is made between the results obtained by ASET and by traditional methodologies for computing suspended-sediment transport in a large river system (Paraná River, Argentina).

Computers & Geosciences↗

Sulfate and sulfide sulfur isotopes (δ34S and δ33S) measured by solution and laser ablation MC-ICP-MS: An enhanced approach using external correction

Isotope ratio measurements using a multi-collector inductively coupled plasma mass spectrometer (MC-ICP-MS) commonly use standard-sample bracketing with a single isotope standard for mass bias correction for elements with narrow-range isotope systems measured by MC-ICP-MS, e.g. Cu, Fe, Zn, and Hg. However, sulfur (S) isotopic composition (δ 34 S) in nature can range from at least − 40 to + 40‰, potentially exceeding the ability of standard-sample bracketing using a single sulfur isotope standard to accurately correct for mass bias. Isotopic fractionation via solution and laser ablation introduction was determined during sulfate sulfur (S sulfate ) isotope measurements. An external isotope calibration curve was constructed using in-house and National Institute of Standards and Technology (NIST) S sulfate isotope reference materials (RM) in an attempt to correct for the difference. The ability of external isotope correction for S sulfate isotope measurements was evaluated by analyzing NIST and United States Geological Survey (USGS) S sulfate isotope reference materials as unknowns. Differences in δ 34 S sulfate between standard-sample bracketing and standard-sample bracketing with external isotope correction for sulfate samples ranged from 0.72‰ to 2.35‰ over a δ 34 S range of 1.40‰ to 21.17‰. No isotopic differences were observed when analyzing S sulfide reference materials over a δ 34 S sulfide range of − 32.1‰ to 17.3‰ and a δ 33 S range of − 16.5‰ to 8.9‰ via laser ablation (LA)-MC-ICP-MS. Here, we identify a possible plasma induced fractionation for S sulfate and describe a new method using external isotope calibration corrections using solution and LA-MC-ICP-MS.

Chemical Geology↗

Terrestrial ecological risk analysis via dietary exposure at uranium mine sites in the Grand Canyon watershed (Arizona, USA)

The U.S. Department of the Interior recently included uranium (U) on a list of mineral commodities that are considered critical to economic and national security. The uses of U for commercial and residential energy production, defense applications, medical device technologies, and energy generation for space vehicles and satellites are known, but the environmental impacts of uranium extraction are not always well quantified. We conducted a screening-level ecological risk analysis based on exposure to mining-related elements via diets and incidental soil ingestion for terrestrial biota to provide context to chemical characterization and exposures at breccia pipe U mines in northern Arizona. Relative risks, calculated as hazard quotients (HQs), were generally low for all biological receptor models. Our models screened for risk to omnivores and insectivores (HQs>1) but not herbivores and carnivores. Uranium was not the driver of ecological risk; arsenic, cadmium, copper, and zinc were of concern for biota consuming ground-dwelling invertebrates. Invertebrate species composition should be considered when applying these models to other mining locations or future sampling at the breccia pipe mine sites. Dietary concentration thresholds (DCTs) were also calculated to understand food concentrations that may lead to ecological risk. The DCTs indicated that critical concentrations were not approached in our model scenarios, as evident in the very low HQs for most models. The DCTs may be used by natural resource and land managers as well as mine operators to screen or monitor for potential risk to terrestrial receptors as mine sites are developed and remediated in the future.

Arizona↗

Determination and prediction of micro scale rare earth element geochemical associations in mine drainage treatment wastes

Acid mine drainage (AMD) has been proposed as a novel source of rare earth elements (REE), a group of elements that includes critical metals for clean energy and modern technologies. REE are sequestered in the Fe–Al–Mn-rich precipitates produced during the treatment of AMD. These AMD solids are typically managed as waste but could be a REE source. Here, results from AMD solids characterization and geochemical modeling are presented to determine the minerals/solid phases that are enriched in REE and identify the mechanism(s) of REE attenuation. AMD solids collected from limestone-based AMD treatment systems were subjected to sequential extraction and synchrotron microprobe analyses to characterize the binding nature of the REE. The results of these analyses indicated REEs were mainly associated with Al or Mn phases. Only selected REE (Gd, Dy) were associated with Fe phases, which were less abundant than Al and Mn phases in analyzed samples. The sequential extractions demonstrated that acidic and/or reducing extractions effectively mobilize REE from the AMD solids evaluated. The observed element associations in solids are consistent with geochemical model results that indicate dissolved REE can be effectively attenuated by adsorption on freshly precipitated Fe, Al, and Mn oxides/hydroxides. The model, which simulates dissolution of CaCO 3 and the precipitation of Fe, Al, and Mn oxides with increased pH, accurately predicts the pH dependent accumulation of dissolved REE with Al, Mn, and Fe oxides/hydroxides in the studied AMD treatment systems. The methods and results presented here can be used to identify conditions favorable for accumulation of REE-enriched AMD solids and possible passive or active treatment(s) to extract REE from AMD. This information can be used to design AMD treatment systems for the recovery of REE and is an opportunity to transform the challenges of addressing polluted mine drainage into an environmental and economic asset.

Pennsylvania↗

Development of web-based organic petrology photomicrograph atlases and internet resources for professionals and students

With advances in web applications, organic petrography and other related disciplines are in need of updated online resources and educational tools to aid professionals and students in the identification and interpretation of macerals. The U.S. Geological Survey (USGS) Organic Petrology Laboratory along with USGS Eastern Energy Resources Science Center Information Technology staff have developed five web atlases containing images of organic matter in geologic materials: 1. an American Society for Testing and Materials (ASTM) Atlas, 2. an Organic Petrology Taxonomy for International Classification (OPTIC) of Coal Macerals Atlas, 3. an Interactive Gulf Coast Photomicrograph Web Atlas (I-Map), 4. an Organic Material in Shales Atlas (Shale), and 5. an interactive Blue/White/Ultraviolet (UV) Light Atlas (Light). Each web atlas contains images of macerals with associated sample and petrographic data collected by the USGS. These webpages will provide means to preserve and circulate petrographic data collected by the USGS for coal and shale samples from all over the world.

International Journal of Coal Geology↗

Coal resources, reserves and peak coal production in the United States

In spite of its large endowment of coal resources, recent studies have indicated that United States coal production is destined to reach a maximum and begin an irreversible decline sometime during the middle of the current century. However, studies and assessments illustrating coal reserve data essential for making accurate forecasts of United States coal production have not been compiled on a national basis. As a result, there is a great deal of uncertainty in the accuracy of the production forecasts. A very large percentage of the coal mined in the United States comes from a few large-scale mines (mega-mines) in the Powder River Basin of Wyoming and Montana. Reported reserves at these mines do not account for future potential reserves or for future development of technology that may make coal classified currently as resources into reserves in the future. In order to maintain United States coal production at or near current levels for an extended period of time, existing mines will eventually have to increase their recoverable reserves and/or new large-scale mines will have to be opened elsewhere. Accordingly, in order to facilitate energy planning for the United States, this paper suggests that probabilistic assessments of the remaining coal reserves in the country would improve long range forecasts of coal production. As it is in United States coal assessment projects currently being conducted, a major priority of probabilistic assessments would be to identify the numbers and sizes of remaining large blocks of coal capable of supporting large-scale mining operations for extended periods of time and to conduct economic evaluations of those resources.

International Journal of Coal Geology↗

Enhanced coal-dependent methanogenesis coupled with algal biofuels: Potential water recycle and carbon capture

Many coal beds contain microbial communities that can convert coal to natural gas (coalbed methane). Native microorganisms were obtained from Powder River Basin (PRB) coal seams with a diffusive microbial sampler placed downhole and used as an inoculum for enrichments with different nutrients to investigate microbially-enhanced coalbed methane production (MECoM). Coal-dependent methanogenesis more than doubled when yeast extract (YE) and several less complex components (proteins and amino acids) were added to the laboratory microcosms. Stimulated coal-dependent methanogenesis with peptone was 86% of that with YE while glutamate-stimulated activity was 65% of that with YE, and a vitamin mix had only 33% of the YE stimulated activity. For field application of MECoM, there is interest in identifying cost-effective alternatives to YE and other expensive nutrients. In laboratory studies, adding algal extract (AE) with lipids removed stimulated coal-dependent methanogenesis and the activity was 60% of that with YE at 27 d and almost 90% of YE activity at 1406 d. Analysis of British Thermal Unit (BTU) content of coal (a measure of potential energy yield) from long-term incubations indicated > 99.5% of BTU content remained after coalbed methane (CBM) stimulation with either AE or YE. Thus, the coal resource remains largely unchanged following stimulated microbial methane production. Algal CBM stimulation could lead to technologies that utilize coupled biological systems (photosynthesis and methane production) that sustainably enhance CBM production and generate algal biofuels while also sequestering carbon dioxide (CO 2 ).

International Journal of Coal Geology↗

Machine learning and data augmentation approach for identification of rare earth element potential in Indiana Coals, USA

Rare earth elements and yttrium (REYs) are critical elements and valuable commodities due to their limited availability and high demand in a wide range of applications and especially in high-technology products. The increased demand and geopolitical pressures motivate the search for alternative sources of REYs, and coal, coal waste, and coal ash are considered as new sources for these critical elements. This research evaluates the REY potential of coals from Indiana (USA). However, although coal data revealed REY potential, it suffered from sparse samples with complete REY measurements. Therefore, we explore the applicability of machine learning (ML) models and data augmentation techniques to demonstrate their applicability to evaluate REY potential in Indiana, and other areas in coal basins, using selected coal parameters (Al2O3, Fe2O3, C, Ash, S, P, Mo, Zn, and As contents) as covariates (indicators). Due to the relatively small sample size with complete REY data in the Indiana Coal Database, two data augmentation techniques (Random Over-Sampling Examples and Synthetic Minority Over-Sampling Technique) were used. Four machine learning algorithms (linear discriminate analysis, support vector machine, random forest, and artificial neural networks) were applied for modeling REY potential as a classification problem. The results show that application of Synthetic Minority Over-Sampling Technique prior to development of the support vector machine (SVM) models generated the best REY classification with an accuracy of 95%. The encouraging results based on Indiana coal data may suggest that a similar approach can be used for other coal basins for screening the locations with REY potential. Those locations then can be targeted for more detailed geochemical surveys to identify most promising areas and evaluate overall REY resources.

Indiana↗

Scanning electron microscopic evaluation of broad ion beam milling effects to sedimentary organic matter: Sputter-induced artifacts or naturally occurring porosity?

Research examining organic-matter hosted porosity has significantly increased during the last decade due to greater focus on understanding hydrocarbon migration and storage in source-rock reservoirs, and technological advances in scanning electron microscopy (SEM) capabilities. The examination of nanometer-scale organic-matter hosted porosity by SEM requires the preparation of exceptionally flat geologic samples beyond the abilities of traditional mechanical polishing, which can deform or otherwise obscure organic surfaces. To meet this demand, broad ion beam (BIB) milling was introduced as a sample preparation technique for SEM petrographic analysis of geologic samples. As with any sample preparation technique, there can be unintended consequences. In this study, we examined the development of nanometer-scale sputter-induced voids caused by BIB milling of thermoset plastic binder material [poly(methyl methacrylate), PMMA] used for the mounting of geologic samples, and artifact sputter-induced voids in the organic matter of Green River Formation and Kimmeridge Clay Formation source rocks. Development of artifact sputter-induced voids was evaluated in relation to variations in the slope of the sample examination surface (0.0–4.9% slope), effectively varying the angle of ion incidence. The results indicate that only minor variations in the angle of ion incidence can generate sputter-induced voids in both PMMA (10.0–386.2 nm diameter sputter-induced voids) and sample sedimentary organic matter (solid bitumen; 12.2–103.6 nm diameter sputter-induced voids). Overall, average artifact pore diameters increased with increasing ion incidence angle within PMMA. Although sputter-induced voids within solid bitumen in the Green River Formation sample were only found in limited extent, the size of these void artifacts falls within the same size range as naturally occurring meso-macroporosity. That is, the pore-like artifacts could easily be misconstrued as naturally developed organic porosity, which is a major concern for SEM-based porosity evaluation methods. This study describes the factors that contribute to the creation of artifact sputter-induced voids, their distinguishing characteristics, and best practices for avoiding the creation of ion-induced artifacts.

International Journal of Coal Geology↗

Exploring the uncertainty of machine learning models and geostatistical mapping of rare earth element potential in Indiana coals, USA

Rare earth elements and yttrium (REEs) have a wide range of applications in high- and low-carbon technologies. The strategic significance of REEs has grown due to their expanding applications in manufacturing industries and the constrained availability of these essential resources. This research explores the applicability of machine learning models and their uncertainty for assessing the REE potential in coal beds using various coal parameters as inputs. The work focuses on developing a predictive model based on geological variables, excluding considerations related to potential shifts in the commodities market. The Indiana Coal Quality Database was used as the data source. The promising and unpromising indicators derived from the outlook coefficient of samples from the database were used as the REE potential indicator for machine learning classification models. The filter-based approach with bootstrap was used to evaluate the importance of the coal parameters and their prediction uncertainties. Four machine learning methods (linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), a data balancing and augmentation approach (Synthetic Minority Over-sampling Technique), and bootstrap resampling techniques were used for building the models and evaluating their prediction capabilities under uncertainty. It was determined that the SVM bootstrap model with ten-times balanced and augmented data provided superior results compared with other models. Finally, stochastic spatial maps of the REE potential within the coal basin were generated using sequential indicator simulation. The spatial maps of the REE potential showed that a 29% area of the Indiana section of the Illinois coal basin has economic potential of REEs, with 90% confidence.

Indiana↗

Structure and morphology of chars and activated carbons obtained from thermal treatment of coal and biomass origin materials, including their wastes: Results from the ICCP Microscopy of Carbon Materials Working Group

This paper describes the evaluation of petrographic textures in char and activated carbon derived from coal, coal by-products and biomass, formed during carbonization and activation processes. This work represents the results of interlaboratory exercises from 2016 to 2022 of the Microscopy of Carbon Materials Working Group in Commission III of the International Committee for Coal and Organic Petrology. The interlaboratory exercises were run on photomicrograph samples. For textural characterization of carbon materials, the existing American Society for Testing and Materials (ASTM) classification system for metallurgical coke was applied. Morphological differences were evaluated in 29 carbon material types, including 22 char samples, and 7 activated carbon (AC) samples obtained experimentally using conventional direct/indirect and microwave heating technologies. This approach gives an extended view on the identification of microporous carbons, and how a certain heat treatment develops a certain optical texture and structure in a raw material. The requested evaluation of carbon materials was related to their porosity, origin, extent, and characteristics, which are particular to each carbon material type. Because carbon matrices can form a wide range of optical textures during heat treatment it is important to demonstrate which carbon occurrences will have a crucial role in industrial applications dominated by adsorption phenomena. The interlaboratory exercises included 17 participants from 14 laboratories. Four sets of digital black and white and colour photomicrographs were distributed, which in total comprised 184 fields of different types of carbon material. The results were evaluated based on four levels: (i) optical texture (isotropic/anisotropic), (ii) optical type and size (punctiform, mosaic, fiber, ribbon, domain), iii) morphology (porous, non-porous/massive), and (iv) particle origin (precursor type). The statistical method applied to evaluate the results was based on “raw agreement indices”. Comparative analyses of the average values of the level of overall agreement showed homogeneity in the results, the mean value was 89%, with a minimum value of 87% and a maximum value of 91% for those who participated in at least three out of four exercises.

International Journal of Coal Geology↗